政府近年對疏濬工程相關議題日漸重視，水利署也致力於掌控各工程計畫執行成效，但因疏濬工程本身不確定性高且牽涉眾多利害關係人，於實際執行時常有成效不彰的狀況發生。於訪談過程中，發現疏濬工程承辦人員面臨新標案時，採用的工期及成本估算方式多為參考去年曾承辦過的案例，再行預測，但近年因氣候變遷、台灣地震、颱風等天災頻傳，地貌常發生變化，若僅參考去年之案例進行評測，恐不符合實際需求，且於工程起始階段，若未將工程項目範圍定義清楚或有缺漏，最終易導致工期與成本增加。參詢相關意見後，本研究決定針對工程起始階段的工期及成本進行推估，首先蒐集河川局自102年至107年雲端資料與疏濬工程支出標、收入標結算書與驗收證明書等歷史數據，並建立一資料庫。完成資料庫建置後，考量量化變數資料可及性，並與專家學者反覆討論後，針對工期與成本模型，整理得六個可量化、且可自歷史資料取得的影響因子，分別為砂、石、土比例、平均土石價、疏濬總量與工程標總成本(履約工期)，接續應用人工智慧技術，分別對疏濬工程工期及成本建構一確定性模型。確立最佳模型後，結合蒙地卡羅模擬法以隨機抽取變數機率方式，將具不確定性之影響因子納入考量，並藉歷史資料經由巨量模擬，獲得與真實數據接近之累積分布函數圖，茲以評估工程於起始階段工期與成本的經驗機率值。最後，為便於管理單位使用，開發一使用者介面，能對工程可能花費天數與成本推算。相關單位透過本研究可掌握主要影響疏濬工程的量化因子，亦能藉由操作開發之使用者介面，於起始階段對工程工期和成本推算時，有一評判基準。

The government has recently begun to value dredging engineering issues. The Water Resources Agency (WRA) seeks to maximize the effectiveness of various engineering projects. However, dredging engineering projects are often ineffective in reality, because dredging engineering has high uncertainty, and always involves many stakeholders. We found that many dredging project contractors submit tenders using construction period and cost estimation methods based on cases that have been undertaken on the last year. However, landform often changes owing to recent climate change, earthquakes, typhoons and other disasters. Therefore, only evaluating the projects from last year is inadequate, and often leads to increase in construction period and cost if the scope of the project is not clearly defined is or missing at the beginning of the construction.Based on previous research, this study aims to estimate the construction period and cost at the beginning of the construction. The first step is to collect the cloud data, outlay, revenue, acceptance certificate and other past data of dredging engineering from 2013 to 2018, and create the database. The next step is to consider the accessibility of quantitative variable data after completing the database, and after repeatedly discussing with experts and scholars.These are the sand, the gravel, the mud ratio, the average price of the muds, the total dredging amount and the total cost of the tender. Artificial intelligence technology is then adopted to build a deterministic model for dredging project duration and cost.

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